🎥 Face Recognition Surveillance System 🕵️‍♂️

**A Face Recognition System, designed for real-time surveillance and locating missing persons or items **

✨ Key Features

• Real-time face recognition using ML Model.
• Surveillance and monitoring for missing persons or items during mass gatherings.
• Easy-to-use web interface built with React and Tailwind CSS.
• Robust backend API using Flask and Python.

🚀 Demo

🎬 Check out a live demo here or watch a preview below:

🛠️ Technologies Used

Frontend:

• React ⚛️ for building the user interface
• Tailwind CSS 💨 for responsive and attractive styling
• Axios for API calls to the backend

Backend:

• Flask 🐍 for building the API
• face_recognition library for facial detection and recognition
• OpenCV for handling image and video processing
• Python as the main backend language

Deployment:

• Docker 🐳 for containerization of Backend

📁 Project Structure

face-recognition-system/
│
├── frontend/
│   ├── public/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── App.tsx
│   │   └── Home.tsx
│   │   └── Main.tsx
│   │   └── navbar.tsx
│   │   └── NotFound.tsx
│   │   └── ReportForm.tsx
│   │   └── SearchMissing.tsx
│   │   └── Survillance.tsx
│   ├── tailwind.config.js
│   └── package.json
│
├── backend/
    ├── app.py
    ├── models/
    ├── static/Images
    └── requirements.tx

⚡ Quick Start

1. Clone the repository:

   git clone [email protected]:kartik-212004/hackathon-iiit.git
   cd hackathon-iiit

2. Backend Setup:

   • Navigate to the backend folder and set up a virtual environment:

     cd backend
     python3 -m venv venv
     source venv/bin/activate  # On Linux/macOS
     .\venv\Scripts\activate  # On Windows

   • Install dependencies:

     pip install -r requirements.txt

3. Frontend Setup:

   • Navigate to the frontend folder and install dependencies:

     cd frontend
     npm install
     npm run dev

4. Run the Application:

   • Start the Flask backend server:

     cd backend
     python app.py

   • Start the React frontend development server:

     cd frontend
     npm start

🐳 Docker Setup (Optional)

You can also run the entire system using Docker for seamless deployment:

1. Build and run the Docker containers:

   cd backend
   docker build -t flask-backend .
   docker run -p 5000:5000 flask-backend

2. Visit http://localhost:3000 for the frontend and http://172.17.0.2:5000/ for the backend.

📷 Screenshots

Face Recognition in Action

🧠 How It Works

1. Face Registration: Known persons’ images are uploaded and stored in the system for future recognition.
2. Real-time Face Detection: The system captures video feeds or images to detect faces.

🏗️ Future Improvements

• Real-time video stream integration for live surveillance.
• Alert System: Notify authorities when a person is identified.

🤝 Contributions

Contributions are always welcome! Feel free to:

1. Fork the repo.
2. Create a feature branch.
3. Submit a pull request with a detailed description of the changes.

👨‍💻 Team-

• THE OGs - GitHub Profile

📄 License

This project is licensed under the MIT License.

⭐ If you like this project, don’t forget to star the repository!

🙌 Acknowledgements

• Special thanks to the open-source community for amazing resources.
• Inspiration from real-world applications of AI in surveillance systems .